Tale of an Oncogene

Research on a key oncogene of melanoma, MITF, moves from seeing it as a rheostat to seeing it as a supercomputer.

The war on cancer was declared fifty years ago, yet effective therapies are only now trickling in. And very few of them can be characterized as cures. What has been going on, and why is the fight so slow? Here I discuss one example, of melanoma and one of its drivers and central players, the gene MITF.

Melanocytes are not really skin cells, but neural crest cells, i.e. originating in the the embryonic neural tube and giving rise to various peripheral neural structures in the spine, gut, and head. One sub-population migrates off into the epidermis to become melanocytes, which generate skin pigment in melanosome packets, which they distribute around to local keratinocytes. Evolutionarily, these cells are apparently afterthoughts, after originally having developed as part of photoreceptor systems. This history, of unusual evolution and extensive developmental migration and eventual invasion into foreign tissues, has obvious implications for their capacity to form cancers later in life, if mutations re-activate their youthful propensities.

 

Above is shown a sketch of some genes known to play roles in melanoma, and key pathways in which they act. In red are oncogenes known to suffer activating mutations that promote cancer progression. In grey are shown additional oncogenes, ones whose oncogenic mutations are simpler loss-of function, not gain of function, events. And green marks ancillary proteins in these pathways that have not (yet) been found as oncogenes of any sort. MITF is a transcription regulator that drives many genes needed for  melanocyte development and melanosome formation. It also influences cell cycle control and cytoskeletal and cell surface features relevant to migration and invasion of other tissues. This post is based mostly on reviews of the molecules active in melanoma, and the more focused story of MITF.

MITF binds to DNA near target genes, often in concert with other proteins, and activates transcription of the local gene (in most cases, though it represses some targets as well). The evidence linking MITF with melanoma and melanocytes is mostly genetic. It is an essential gene, so complete deletions are lethal. But a wide variety of "mi" mutations in mice and in humans lead to unusual phenotypes like white hair color, loss of hearing, large head formation, small blue eyes, osteopetrosis, and much else. Originally researchers thought there were several different genes involved, but they all resolved down to one complex locus, now called MITF, for mi transcription factor. Certain hereditary mutations also predispose to melanoma, as do some spontaneous mutations. That the dose of MITF also correlates with how active and aggressive a melanoma is also contributes to the recognition that MITF is central to the melanocyte fate and behavior, and also one of the most central players in the disease of melanoma.

The MITF gene spreads over 229,000 base pairs, though it codes for a protein of only 419 amino acids. The gene contains nine alternate transcription start sites, 18 exons (coding regions), and five alternate translation start sites, as sketched above. This structure allows dozens of different forms of the protein to be produced in different tissues and settings, via alternative splicing. The 1M form (above, bottom) is the main one made in melanocytes. Since the gene is essential, mutations that have the phenotypes mentioned above tend to be very small, affecting one amino acid or one splice site, or perhaps truncating translation near the end of the protein. Upstream of the MITF gene and in some of its introns, there are dozens of DNA sites that bind other regulators, which either activate or repress MITF transcription in response to developmental or environmental cues. For example, a LEF1/TCF site binds the protein LEF1, which receives signals from WNT1, which is a central developmental regulator, driving proliferation and differentiation of melanocytes from the stem neural crest cells.

That is just the beginning of MITF's complexity, however. The protein contains in its sequence codes for a wide array of modifications, by regulatory protein kinases (that attach phosphate groups), and other modifiers like SUMO-ylation and ubiquitination. Key cellular regulators like GSK3, AKT, RSK, ERK2, and TAK kinases each attach phosphates that affect MITF's activity. Additionally, MITF interacts with at least a dozen proteins, some of which also bind DNA and alter its target gene specificity, and others that cooperate to activate or repress transcription. One of the better-known signaling inputs is indirectly from the kinase BRAF1, which is a target of the first precision melanoma-fighting drugs. BRAF1 is mutated in half of melanoma cases, to a hyper-active form. It is a kinase responsive to growth factors, generally, and activates a core growth-inducing (MAP) kinase cascade (as shown above), among other pathways. BRAF1 has several effects on MITF by these pathways, but the dominant one seems to be its phosphorylation and activation of PAX3, which is a DNA-binding regulator that activates the MITF gene (and is, notably, absent from the summary figure above, showing how dynamic this field remains). Thus inhibition of BRAF1, which these precision drugs do, effectively reduces MITF expression, most of the time.

Then there are the gene targets of MITF, of which there are thousands, including dozens known to have significant developmental, cell cycle, pigment synthesis, cytoskeletal, and metabolic effects. All this is to say that this one gene participates in a bewilderingly complex network of activities only some of which are recognized to date, and none of which are understood at the kind of quantitative level that would allow for critical modeling and computation of the system. What has been found to date has led to a "switch", or rheostat hypothesis. One of the maddening aspects of melanoma is its resistance to therapy. This is thought in part to be due to this dynamic rheostat, which allows levels of MITF to vary widely and send individual cancer cells reversibly into several different states. At high levels of MITF, cancer cells are pigmented and proliferative (and sensitive to BRAF1 inhibition). But at medium levels of MITF, they revert more to their early migratory behavior, and become metastatic and invasive. So melanoma benefits from a diversity of cell types and states, dynamically switching between states that are both variable in their susceptibility to therapies like anti-BRAF1, and also maximally damaging in their proliferation and ranging activities (diagrammed below).

The theme that comes out of all this is enormous complexity, a complexity that only deepens the more one studies this field. It is a typical example in biology, however, and can be explained by the fact that we are a product of 4 billion years of evolution. The resulting design is far from intelligent- rather, it is a compendium of messy contraptions, historical compromises, and accreted mechanisms. We are very far from having the data to construct proper models that would critically analyze these systems and provide accurate predictions of their behavior. It is not really a computational issue, but a data issue, given the vast complexity we are faced with. Scientists in these fields are still thinking in cartoons, not in equations. 

But there are shortcuts of various kinds. One promising method is to analyze those patients who respond unusually well to one of the new precision treatments. They typically carry some hereditary alteration in some other pathway that in most people generates resistance or backup activity to the one that was drug-treated. If their genomes are fully sequenced and analyzed in depth, they can provide insight into what other pathway(s) may need to be targeted to achieve effective combination treatment. This is a lesson from the HIV and tuberculosis treatment experiences- that the redundancy and responsiveness of biological systems calls for multiple targets and multiple treatments to meet complex disease challenges.

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